In point of fact it is already known to permanently monitor tire pressures of a vehicle. These pressure measurements (possibly corrected for the temperature and the aging of the tire or for any other parameter) are processed by a computer. A warning signal is emitted when a tire pressure is abnormal. The computer that processes the pressure measurements may be fitted onto the wheel itself or at any appropriate point in the vehicle.
The pressure measurements are carried out by a specific sensor associated with each wheel. This sensor sends, to a remote computer, the pressure measurement associated with a code that identifies the sensor. Of course, it is necessary for the computer to know how to assign a position on the vehicle to this identifying code. Thus, after processing, the computer must be capable of stating that the pressure measurement associated with the identifying code X comes from the right front wheel (for example). To do this, it is necessary for the computer to learn the position of the sensor and its identifying code.
This learning may be carried out manually. For example, the computer is placed in learning mode and requests the codes of each pressure sensor in a pre-established order. However, this learning procedure is relatively slow and must also be repeated each time a tire is changed. It has the drawback of requiring the driver to input data into the vehicle's computer. If the driver forgets to store the new code after a tire change, there is a risk of an error regarding the position of a wheel with abnormal pressure. This may have serious consequences.
It would seem opportune to automatically carry out this learning procedure during running of the vehicle. In particular, it is already known to correlate a radiofrequency signal from the sensors with a wheel position, or else to position, close to each wheel, low-frequency/radiofrequency antennas that, by two-way communication, make it possible to identify the position of the wheels, etc.
However, these various automatic wheel-position learning methods have the drawback that they require a complex and expensive architecture to be installed (antennas close to the wheels, two-way communication) or mathematical processing that is very complicated and difficult to make reliable (correlation between power of the radiofrequency signal and the wheel position.